An Automated Framework for Gaming Platform to Test Multiple Games

Abstract

Game testing is a necessary but challenging task for gaming platforms. Current game testing practice requires significant manual effort. In this paper, we proposed an automated game testing framework combining adversarial inverse reinforcement learning algorithm with evolutionary multi-objective optimization. This framework aims to help gaming platform to assure market-wide game qualities as the framework is suitable to test different games with minimum manual customization for each game.